Dremio to Spotfire Integration

How Datrise loads Dremio into Spotfire

Datrise syncs Dremio's datasets, tables, reflections, query jobs, sources, and table schemas into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

Dremio: Lakehouse query engine over open table formats.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Dremio entities map to Spotfire

Dremio entity	Spotfire object	Notes
datasets	`dremio_datasets`	id PK · custom fields → flattened columns for visualizations
tables	`dremio_tables`	id PK · linked to dremio_datasets
reflections	`dremio_reflections`	id PK · linked to dremio_datasets
query jobs	`dremio_query_jobs`	id PK · linked to dremio_datasets

FAQ

How does Datrise handle Dremio's custom fields in Spotfire?

Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.

How does the Dremio to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

Early access

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